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Record W1991558646 · doi:10.1007/s13280-015-0628-x

Soil test phosphorus and cumulative phosphorus budgets in fertilized grassland

2015· article· en· W1991558646 on OpenAlexafffundabout
Aimé J. Messiga, Noura Ziadi, Claire Jouany, Perttu Virkajärvi, Raija Suomela, Sokrat Sinaj, Gilles Bélanger, Ciprian Stroïa, Christian Morel

Bibliographic record

VenueAMBIO · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsAgriculture and Agri-Food CanadaTrent University
FundersAgriculture and Agri-Food CanadaAgroscopeInstitut National de la Recherche Agronomique
KeywordsGrasslandDeflection (physics)Human fertilizationEnvironmental scienceMathematicsAgronomyBiologyPhysics

Abstract

fetched live from OpenAlex

We analyzed the linearity of relationships between soil test P (STP) and cumulative phosphorus (P) budget using data from six long-term fertilized grassland sites in four countries: France (Ercé and Gramond), Switzerland (Les Verrières), Canada (Lévis), and Finland (Maaninka and Siikajoki). STP was determined according to existing national guidelines. A linear-plateau model was used to determine the presence of deflection points in the relationships. Deflection points with (x, y) coordinates were observed everywhere but Maaninka. Above the deflection point, a significant linear relationship was obtained (0.33 < r (2) < 0.72) at four sites, while below the deflection point, the relationship was not significant, with a negligible rate of STP decrease. The relationship was not linear over the range of STP encountered at most sites, suggesting a need for caution when using the P budget approach to predict STP changes in grasslands, particularly in situations of very low P fertilization. Our study provides insights and description of a tool to improve global P strategies aimed at maintaining STP at levels adequate for grassland production while reducing the risk of P pollution of water.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.230
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations32
Published2015
Admission routes3
Has abstractyes

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Same venueAMBIOSame topicSoil and Water Nutrient DynamicsFrench-language works237,207